Systems and methods for managing public place in smart city

ABSTRACT

The disclosure provides a method for managing a public place in a smart city. The method may comprise obtaining pedestrian distribution information in a preset area during a current time period. The method may comprise determining, based on the pedestrian distribution information, at least one area location in the preset area for a future time period, and a population flow load of the area location may be greater than a first threshold. The method may comprise determining, based on the area location, prompt information. The method may comprise sending the prompt information to a user platform through a service platform.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application claims priority of Chinese Patent Application No.202210292445.6, filed on Mar. 24, 2022, the entire contents of which areincorporated herein by reference.

TECHNICAL FIELD

The present disclosure generally relates to the field of Internet ofThings (IoT) and cloud platforms, and in particular, to systems andmethods for managing public place in smart city.

BACKGROUND

With the development of information science technologies, the concept ofthe cloud platforms and the applications of the cloud platforms in IoTare mentioned by more and more people. A cloud platform service modewith cloud computing as a core service may solve the problem ofinsufficient information processing capabilities of various platforms ofthe IoT through scale gain and resource sharing. The introduction of thecloud platforms may provide efficient, dynamic, and massively scalablecomputing capabilities for the IoT, enabling a user-led IoT to runbetter and more efficiently. In the social life, public places are oftencrowded with people or have a large population flow. Users are requiredto queue or wait for a long time in such public places, while otherpublic places are sparsely populated and the population flow is small.In this case, the population flow of the plurality of public places maynot be balanced and the gathering of people may not be avoided.

Therefore, it is desirable to provide methods and systems for managingpublic place in smart city. The IoT and the cloud platforms may be usedto manage a plurality of public places as a whole to avoid the gatheringof people and reduce the time of queuing or waiting for users.

SUMMARY

One aspect of the embodiments of the present disclosure provides amethod for measuring energy of natural gas components. The method maycomprise obtaining pedestrian distribution information in a preset areaduring a current time period. The method may comprise determining, basedon the pedestrian distribution information, at least one area locationin the preset area for a future time period, and a population flow loadof the area location may be greater than a first threshold. The methodmay comprise determining, based on the area location, promptinformation. The method may comprise sending the prompt information to auser platform through a service platform.

In some embodiments, the obtaining pedestrian distribution informationin a preset area during a current time period may include obtaining thepedestrian distribution information through a management platformdatabase, the management platform database obtaining data through atleast one management sub-platform database.

In some embodiments, the at least one management sub-platform databasemay correspond to at least one management sub-platform. The at least onemanagement sub-platform may include at least one of a parking lotmanagement platform, a park management platform, a subway managementplatform, a bus management platform, a museum management platform, astadium management platform, or a shopping mall management platform. Theat least one management sub-platform may obtain data through thecorresponding at least one management sub-platform database.

In some embodiments, the at least one management sub-platform databasemay obtain data from an object platform through a sensor networkplatform database, and the object platform may include at least one of aticket checking device, a camera monitoring device, or an unmannedaerial vehicle (UAV) device.

In some embodiments, a subway management platform database correspondingto the subway management platform may obtain data from the ticketchecking device through the sensor network platform database.

In some embodiments, the determining, based on the pedestriandistribution information, at least one area location in the preset areafor a future time period may include determining, by processing thepedestrian distribution information through an area location predictionmodel, the at least one area location. The area location predictionmodel may include a graph neural network model. A graph input into thegraph neural network model may include at least two nodes and at leastone edge, each of the at least two nodes may include at least one publicplace site, and the at least one edge may include a relationship betweenthe at least two nodes. A node characteristic of the each node mayinclude a count of entrances and exits of the at least one public placesite, distribution positions of the entrances and the exits, timeinformation, node environment information, holiday information, or asize of a parking lot. An edge characteristic of the at least one edgemay include a relationship strength vector.

In some embodiments, the at least one edge of the graph input into thegraph neural network model may be determined by a process. The processmay include obtaining, based on a knowledge map, a distance betweennodes of the knowledge map and a count of hops between the nodes of theknowledge map. The process may include determining whether the distancebetween the nodes of the knowledge map is less than a second thresholdand whether the count of hops between the nodes of the knowledge map isless than a third threshold. In response to determining that thedistance between the nodes of the knowledge map is less than the secondthreshold and the count of hops between the nodes of the knowledge mapis less than the third threshold, the process may include determining,based on the nodes of the knowledge map, a connecting line between thenodes of the knowledge map as the edge of the graph input into the graphneural network model.

In some embodiments, the edge characteristic of the edge of the graphinput into the graph neural network model may include a first transitionprobability characteristic.

In some embodiments, the knowledge map may include an edge with areachable relationship, and the first transition probabilitycharacteristic may be determined through a second transition probabilitycharacteristic of an edge with a direct reachable relationship.

Another aspect of the embodiments of the present disclosure provides asystem for managing a public place in a smart city. The system maycomprise at least one storage device including a set of instructions,and at least one processor in communication with the at least onestorage device, wherein when executing the set of instructions, the atleast one processor is configured to cause the system to obtainpedestrian distribution information in a preset area during a currenttime period. The at least one processor is configured to cause thesystem to determine, based on the pedestrian distribution information,at least one area location in the preset area for a future time period,and a population flow load of the area location may be greater than afirst threshold. The at least one processor is configured to cause thesystem to determine, based on the area location, prompt information. Theat least one processor is configured to cause the system to send theprompt information to a user platform through a service platform.

A non-transitory computer readable medium storing instructions, whenexecuted by at least one processor, causing the at least one processorto implement a method, and the method may comprise obtaining pedestriandistribution information in a preset area during a current time period.The method may comprise determining, based on the pedestriandistribution information, at least one area location in the preset areafor a future time period, and a population flow load of the arealocation may be greater than a first threshold. The method may comprisedetermining, based on the area location, prompt information. The methodmay comprise sending the prompt information to a user platform through aservice platform.

Additional features will be set forth in part in the description whichfollows, and in part will become apparent to those skilled in the artupon examination of the following and the accompanying drawings or maybe learned by production or operation of the examples. The features ofthe present disclosure may be realized and attained by practice or useof various aspects of the methodologies, instrumentalities, andcombinations set forth in the detailed examples discussed below.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an application scenario of anIoT system for managing a public place in a smart city according to someembodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating an IoT system for managing apublic place in a smart city according to some embodiments of thepresent disclosure;

FIG. 3 is a schematic flowchart illustrating an IoT system for managinga public place in a smart city according to some embodiments of thepresent disclosure;

FIG. 4 is a schematic diagram illustrating a public place managementplatform, a sensor network platform, and an object platform according tosome embodiments of the present disclosure;

FIG. 5 is a schematic diagram illustrating a determination of an arealocation based on pedestrian distribution information according to someembodiments of the present disclosure; and

FIG. 6 is a schematic flowchart illustrating a determination of an edgeof a graph input into a graph neural network model according to someembodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. Obviously, drawings described below are onlysome examples or embodiments of the present disclosure. Those skilled inthe art, without further creative efforts, may apply the presentdisclosure to other similar scenarios according to these drawings. Itshould be understood that the purposes of these illustrated embodimentsare only provided to those skilled in the art to practice theapplication, and not intended to limit the scope of the presentdisclosure. Unless obviously obtained from the context or the contextillustrates otherwise, the same numeral in the drawings refers to thesame structure or operation.

It will be understood that the terms “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, sections, or assemblies ofdifferent levels in ascending order. However, the terms may be displacedby other expressions if they may achieve the same purpose.

The terminology used herein is for the purposes of describing particularexamples and embodiments only and is not intended to be limiting. Asused herein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “include” and/or“comprise,” when used in this disclosure, specify the presence ofintegers, devices, behaviors, stated features, steps, elements,operations, and/or components, but do not exclude the presence oraddition of one or more other integers, devices, behaviors, features,steps, elements, operations, components, and/or groups thereof.

The flowcharts used in the present disclosure illustrate operations thatsystems implement according to some embodiments of the presentdisclosure. It is to be expressly understood, the operations of theflowcharts may be implemented not in order. Conversely, the operationsmay be implemented in an inverted order, or simultaneously. Moreover,one or more other operations may be added to the flowcharts. One or moreoperations may be removed from the flowcharts.

FIG. 1 is a schematic diagram illustrating an application scenario of anIoT system for managing a public place in a smart city according to someembodiments of the present disclosure.

In some embodiments, the application scenario 100 of an IoT system formanaging a public place in a smart city may include a processing device110, a network 120, a storage device 130, a terminal 140, and a publicplace 150. In some embodiments, components in the application scenario100 may be connected with and/or in communication with each other viathe network 120 (e.g., a wireless connection, a wired connection, or acombination thereof). For example, the processing device 100 may beconnected to the storage device 130 via the network 120.

In some embodiments, the processing device 110 may process informationand/or data related to the application scenario 100 of an IoT system formanaging a public place in a smart city to perform one or more functionsdescribed in the present disclosure. For example, the processing device110 may determine one or more area locations in a preset area for afuture time period based on pedestrian distribution information. In someembodiments, the processing device 110 may include one or moreprocessing engines (e.g., a single-chip processing engine or amulti-chip processing engine). Merely as an example, the processingdevice may include a Central Processing Unit (CPU). The processingdevice 110 may process data, information, and/or processing resultsobtained from other devices or components of the system, and executeprogram instructions based on the data, information, and/or processingresults to perform one or more functions described in the presentdisclosure.

The network 120 may include any suitable network that may facilitateinformation and/or data exchange of the IoT system for managing a publicplace. The information and/or data may be exchanged between one or morecomponents (e.g., the storage device 130, the processing device 110, theterminal 140) of the IoT system for managing a public place via thenetwork 120. For example, the network 120 may send prompt information toa user platform via a service platform. In some embodiments, the network120 may be any one or more of a wired network or a wireless network. Insome embodiments, the network 120 may include one or more network accesspoints. For example, the network 120 may include wired or wirelessnetwork access points. In some embodiments, the network 120 may be invarious topologies such as point-to-point, shared, centralized, or thelike, or a combination of a plurality of topologies.

The storage device 130 may be used to store data, instructions, and/orany other information. In some embodiments, the storage device 130 maystore data and/or information obtained from the processing device 110,the terminal 140, or the like. For example, the storage device 130 maystore videos of population flow, the pedestrian distributioninformation, or the like. In some embodiments, the storage device 130may be arranged in the processing device 110. In some embodiments, thestorage device 130 may include a mass storage, a removable storage, orthe like, or any combination thereof.

The terminal 140 may be a device or other entity directly related to themanagement of a public place. In some embodiments, the terminal 140 maybe a terminal used by a manager of a public place, such as a terminalused by a staff of the management of the public place. In someembodiments, the terminal 140 may include a mobile device 140-1, atablet 140-2, a notepad 140-3, a laptop 140-4, or the like, or anycombination thereof. In some embodiments, the mobile device 140-1 mayinclude a smartphone, a smart paging device, or the like, or other smartdevices. The mobile device 140-1 may interact with other components inthe service platform via the network 120. For example, the mobile device140-1 may receive the information sent by the service platform that thepopulation flow in one or more areas is relatively large, and people maynot go to the areas as much as possible. In some embodiments, theterminal 140 may include other smart terminals, such as a wearable smartterminal or the like. The terminal 140 may be a smart terminal, or maybe an entity including a smart terminal, for example, a managementdevice including a smart computer.

The public place 150 may refer to various places providing publicservices to users. For example, the public place may include a shoppingmall, a parking lot, a subway, and/or a park, or the like. A certainrelationship may exist among the various public places, for example, adistance relationship among the various public places, a relationshipwhether the various public places may be reachable (e.g., the shoppingmall may be reached via the subway), or the like.

It should be noted that the above descriptions are merely provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. For example, the application scenario 100 of theIoT system for managing a public place may implement similar ordifferent functions on other devices. However, those variations andmodifications do not depart from the scope of the present disclosure.

The IoT system may be an information processing system including part orall of a user platform, a service platform, a management platform, asensor network platform, and an object platform. The user platform mayrefer to a user-led platform that may obtain user requirements andfeedback information to the users. The service platform may refer to aplatform that may provide the users with input and output services. Themanagement platform may realize an overall planning and coordination ofthe connection and cooperation among various functional platforms (suchas the sensor network platform and the object platform), gather theinformation of an IoT system, and provide perception management andcontrol management functions for the IoT system. The sensor networkplatform may realize the connection between the management platform andthe object platform, and play a role of sensing communication ofperception information and control information. The object platform maybe a functional platform for the generation of the perceptioninformation and the execution of the control information.

The information processing in the IoT system may be divided into aprocessing of the perception information and a processing of the controlinformation, and the control information may be generated based on theperception information. The processing of the perception information maybe that the object platform obtains the perception information andtransmits the perception information to the management platform throughthe sensor network platform. The control information may be sent by themanagement platform to the object platform through the sensor networkplatform to realize the control of a corresponding object.

In some embodiments, when the IoT system is applied to city management,the IoT system may be referred to as an IoT system of a smart city.

FIG. 2 is a schematic diagram illustrating an IoT system for managing apublic place in a smart city according to some embodiments of thepresent disclosure. As shown in FIG. 2 , the IoT system 200 for managinga public place in a smart city may be implemented based on an IoTsystem. The IoT system 200 for managing a public place in a smart citymay include a user platform 210, a service platform 220, a public placemanagement platform 230, a sensor network platform 240, and an objectplatform 250. In some embodiments, the IoT system 200 for managing apublic place in a smart city may be a part of the processing device 110or implemented through the processing device 110.

The user platform 210 may refer to a user-led platform. For example, theuser platform 210 may obtain an input instruction from the user througha terminal (e.g., the terminal 140), and inquire about the pedestriandistribution information. As another example, the user platform mayfeedback the pedestrian distribution information to the user.

The service platform 220 may refer to a platform providing input andoutput services to the user. For example, the service platform may sendprompt information or the like to the user platform. In someembodiments, the service platform 220 may include a service informationintegrated management platform, a management platform database, aplurality of service sub-platforms, and a plurality of databasescorresponding to the plurality of service sub-platforms, respectively.For example, the plurality of service sub-platforms may include asub-platform 1, a sub-platform 2, . . . , a sub-platform n, or the like.The corresponding plurality of databases may be database 1, database 2,. . . , database n, or the like. The service platform 220 may realizethe information interaction with the plurality of service sub-platforms,the user platform, and the public place management platform through theservice platform database. The service platform 220 may obtain therelevant information of the user platform through the serviceinformation integrated management platform.

The public place management platform 230 may coordinate the connectionand cooperation among various functional platforms, and gather all theinformation of the IoT system. The public place management platform 230may be a platform providing perception management and control managementfunctions for the IoT operating system. For example, the public placemanagement platform 230 may obtain the pedestrian distributioninformation in a preset area for a future time period. At least one arealocation in the preset area for the future time period may be determinedbased on the pedestrian distribution information, and based on the arealocation, prompt information may be determined. The prompt informationmay be sent to the user platform through the service platform. Thepublic place management platform 230 may include the processing device110 shown in FIG. 1 as well as other components. In some embodiments,the public place management platform 230 may be a remote platformoperated by a manager, artificial intelligence, or a pre-set rule. Insome embodiments, the public place management platform 230 may include amanagement information integrated management platform. The public placemanagement platform 230 may obtain the relevant information of theservice platform database through the management information integratedmanagement platform.

In some embodiments, as shown in FIG. 4 , the public place managementplatform 230 may include a management platform database 231, amanagement sub-platform 232, a management sub-platform database 233, orthe like. For more details about the management platform database 231,the management sub-platform 232, and the management sub-platformdatabase 233, refer to FIG. 3 , FIG. 4 , and related descriptions.

The sensor network platform 240 may refer to a functional platformmanaging the sensing communication. In some embodiments, the sensornetwork platform 240 may connect the public place management platform230 and the object platform 250 to realize the functions of the sensingcommunication of the perception information and the control information.In some embodiments, the sensor network platform 250 may include aplurality of sensor network sub-platforms and a plurality of databasescorresponding to the plurality of sensor network sub-platforms,respectively. For example, the plurality of sensor network sub-platformsmay include a sub-platform 1, a sub-platform 2, . . . , a sub-platformn, or the like. The corresponding plurality of databases may be database1, database 2, . . . , database n, or the like. In some embodiments, thesensor network platform 240 may include a sensing information integratedmanagement platform. The sensor network platform 240 may realize theinformation interaction with the public place management platformthrough the sensing information integrated management platform.

The object platform 250 may refer to a functional platform generatingthe perception information. In some embodiments, the object platform 250may obtain information, for example, population flow information may beobtained from different preset areas, or the like.

More details about the sensor network platform 240 and the objectplatform 250 may refer to FIG. 3 , FIG. 4 , and the relateddescriptions.

In some embodiments, the IoT system 200 for managing a public place of asmart city may be applied to various scenarios of the public placemanagement. In some embodiments, the IoT system 200 for managing apublic place in a smart city may separately acquire relevant data of thepublic place in various scenarios to obtain public place managementpolicies in each scenario, for example, data about a population flow, anopening hour, an open area, a ticket price, maintenance information, orthe like. In some embodiments, the IoT system 200 for managing a publicplace in a smart city may obtain a public place management policy for anentire area (e.g., the entire city) based on various related data of thepublic place in various scenarios.

The various scenarios of public place management may include, forexample, a parking lot scenario, a park scenario, a subway scenario, orthe like. For example, the various scenarios of public place managementmay include a parking lot public place management, a park public placemanagement, a subway public place management, or the like. It should benoted that the above scenarios are merely provided for the purposes ofillustration, and not intended to limit the specific applicationscenarios of the IoT system 200 for managing a public place in a smartcity. For persons having ordinary skills in the art, the IoT system 200for managing a public place in a smart city may be applied to any othersuitable scenarios on based on the descriptions of the presentdisclosure.

In some embodiments, the IoT system 200 for managing a public place in asmart city may be applied to the parking lot public place management.When applied to the parking lot public place management, the objectplatform 250 may be used to collect data related to the parking lot,such as data related to traffic flow at different times, an openinghour, a ticket price, whether maintenance is required, or the like. Theobject platform 250 may upload the collected data related to the parkinglot to the sensor network platform 240 (e.g., the sensor networkplatform database). The sensor network platform 240 may aggregate thecollected data. For example, the sensor network platform 240 may dividethe collected data based on the traffic flow at different times, anarea, or the like. The sensor network platform 240 may upload theaggregated and processed data to the parking lot management platformdatabase through the sensor network platform database. The public placemanagement platform may obtain relevant data through the parking lotmanagement platform database. Policies or instructions related to theoperation and management of the parking lot may be determined based onthe processing of the collected data, such as instructions for the countof remaining vacancies in the parking lot, closing instructions, or thelike.

In some embodiments, the IoT system 200 for managing a public place in asmart city may be applied to the park public place management. Whenapplied to the park public place management, the object platform 250 maybe used to collect related data of the park. For example, the relateddata of the park may be population flow at different times, an openinghour, a ticket price, maintenance information, or the like. The objectplatform may upload the collected data of the park to the sensor networkplatform database. The sensor network platform 240 may aggregate thecollected data. For example, the sensor network platform 240 may dividethe collected data based on the population flow at different times, anarea, or the like. The sensor network platform 240 may upload theaggregated and processed data to the park management platform databasethrough the sensor network platform database. The public placemanagement platform may obtain relevant data through the park managementplatform database. Policies or instructions related to the operation andmanagement of the park may be determined based on the processing of thecollected data, such as an instruction that the park has a largepopulation flow load during a certain period of time, an instructionthat the park is open from 09:00 to 18:00, an instruction thatrecreational facilities of the park need maintenance, or the like.

In some embodiments, the IoT system 200 for managing a public place in asmart city may be applied to the subway public place management. Whenapplied to the subway public place management, the object platform 250may be used to collect related data of the subway. For example, therelated data of the subway may be population flow at different times, anopening hour, a ticket price, maintenance information, or the like. Theobject platform may upload the collected data of the subway to thesensor network platform database. The sensor network platform 240 mayaggregate the collected data. For example, the sensor network platform240 may divide the collected data based on the population flow atdifferent times, the ticket price at different stations, whether thesubway needs maintenance, or the like. The sensor network platform 240may upload the aggregated and processed data to the subway managementplatform database through the sensor network platform database. Thepublic place management platform may obtain relevant data through thesubway management platform database. Policies or instructions related tothe operation and management of the subway may be determined based onthe processing of the collected data, such as an instruction forpopulation flow of the current time period, an instruction that thesubway is open from 06:00 to 22:00, an instruction that facilities ofthe subway need maintenance, or the like.

In some embodiments, the IoT system 200 for managing a public place in asmart city may be composed of a plurality of subsystems for managing apublic place in a smart city, each of which may be applied to ascenario. The IoT system 200 for managing a public place in a smart citymay comprehensively manage and process the data acquired and output byeach subsystem, thereby obtaining relevant policies or instructions forassisting the management of the public place in the smart city.

For example, the IoT system 200 for managing a public place in a smartcity may include a subsystem applied to the parking lot public placemanagement, a subsystem applied to the park public place management, anda subsystem applied to the subway public place management. the IoTsystem 200 for managing a public place in a smart city may serve as anupper-level system of each subsystem.

The following may illustrate the IoT system 200 for managing a publicplace in a smart city managing each subsystem and obtaining policies formanaging a public place in a smart city based on corresponding dataobtained from the subsystems:

The IoT system 200 for managing a public place in a smart city mayobtain relevant data about the traffic flow at different times, theopening hour, the ticket price, whether maintenance is required, or thelike, based on the subsystem of the parking lot public place management.The IoT system 200 for managing a public place in a smart city mayobtain relevant data about the population flow at different times, theopening hour, the ticket price, or the like, based on the subsystem ofthe park public place management. The IoT system 200 for managing apublic place in a smart city may obtain relevant data about thepopulation flow at different times, the opening hour, the ticket price,whether maintenance is required, or the like, based on the subsystem ofthe subway public place management.

When the IoT system 200 for managing a public place in a smart cityobtains the data mentioned above, a plurality of object platforms may beseparately set corresponding to each subsystem to obtain data.

After the IoT system 200 for managing a public place in a smart cityobtains the data mentioned above, the sensor network platform 240 mayaggregate the collected data. The sensor network platform 240 may uploadthe aggregated and processed data to the management sub-platformdatabase through the sensor network platform database. The public placemanagement platform 230 may obtain data through the managementsub-platform database. Predicted data related to the public placemanagement may be determined by the public place management platform 230based on the processing of the collected data.

For example, the sensor network platform 240 may determine a predictionof the population flow from the parking lot to a nearby park based on aninstruction of vacancies remaining in the parking lot, a closinginstruction, or the like. The sensor network platform 240 may determinea prediction of the population flow from the subway to the nearby parkbased on an instruction of the population flow data of the subway in thecurrent time period. The sensor network platform 240 may determine thepopulation flow in different areas of the park in a future time periodbased on the population flow of the park in the current time period. Thesensor network platform 240 may upload the data mentioned above to thepublic place management platform through the sensor network platformdatabase. The public place management platform may predict thepopulation flow from the parking lot to the nearby park, the populationflow from the subway to the nearby park, and the population flow indifferent areas of the park in the future time period based on the datamentioned above. The public place management platform may furtherdetermine an area location of the park with a larger population flowload in the future time period.

As another example, the sensor network platform 240 may determine theprediction of the population flow from the subway to the nearby parkbased on the population flow data of the subway in the current timeperiod, or the like. The sensor network platform 240 may determine thepopulation flow of the park at different time periods and themaintenance requirements of the recreational facilities in the parkbased on the relevant data of the park, such as the opening hour, themaintenance information, or the like. The sensor network platform 240may upload the data mentioned above to the public place managementplatform through the sensor network platform database. The public placemanagement platform may predict the population flow from the subway tothe nearby park, the population flow of the park at different timeperiods, the opening hour of the park, and the maintenance informationof the recreational facilities in the park based on the data mentionedabove. The public place management platform may further determine themaintenance time of the recreational facilities in the park.

For persons having ordinary skills in the art, after understanding theprinciples of the IoT system 200 for managing a public place in a smartcity, the IoT system 200 for managing a public place in a smart city maybe implemented in any other suitable scenarios without departing fromthe principles of the IoT system 200.

The following may take the IoT system 200 for managing a public place ina smart city applied to the population flow management of the publicplace as an example to illustrate the IoT system 200 for managing apublic place in a smart city in detail.

The public place management platform 230 may be configured to obtainpedestrian distribution information in a preset area during a currenttime period, and determine, based on the pedestrian distributioninformation, at least one area location in the preset area for a futuretime period, wherein a population flow load of the area location isgreater than a first threshold. The public place management platform 230may also be configured to determine, based on the area location, promptinformation, and send the prompt information to a user platform througha service platform.

In some embodiments, the public place management platform 230 may alsobe configured to obtain the pedestrian distribution information througha management platform database. The management platform database mayobtain data through at least one management sub-platform database.

In some embodiments, the at least one management sub-platform databasemay correspond to at least one management sub-platform. The at least onemanagement sub-platform may include at least one of a parking lotmanagement platform, a park management platform, a subway managementplatform, a bus management platform, a museum management platform, astadium management platform, or a shopping mall management platform. Theat least one management sub-platform may obtain data through thecorresponding at least one management sub-platform database.

In some embodiments, the at least one management sub-platform databasemay obtain data from an object platform through a sensor networkplatform database, and the object platform may include at least one of aticket checking device, a camera monitoring device, and an unmannedaerial vehicle (UAV) device.

In some embodiments, a subway management platform database correspondingto the subway management platform may obtain data from a ticket checkinggate through the sensor network platform database.

For more descriptions about the management sub-platform, the sensornetwork platform, and the object platform, refer to FIG. 4 and therelated descriptions, which is not repeated herein.

In some embodiments, the public place management platform 230 may beconfigured to determine the at least one area location based on aprocessing of the pedestrian distribution information through an arealocation prediction model. The area location prediction model mayinclude a graph neural network model. A composition of a graph inputinto the graph neural network model may include at least two nodes andat least one edge. Each of the at least two nodes may include at leastone public place site, and the at least one edge may include arelationship between the at least two nodes. A node characteristic ofthe node may include a count of entrances and exits of the at least onepublic place site, distribution positions of the entrances and theexits, time information, node environment information, holidayinformation, and a parking lot size. An edge characteristic of the atleast one edge may include a relationship strength vector. For moredetails about the area location prediction model and the graph neuralnetwork model, refer to FIG. 5 and the related descriptions, which isnot repeated herein.

In some embodiments, the public place management platform 230 may beconfigured to obtain, based on a knowledge map, a distance between nodesof the knowledge map and a count of hops between the nodes of theknowledge map, and determine whether the distance between the nodes ofthe knowledge map is less than a second threshold and whether the countof hops between the nodes of the knowledge map is less than a thirdthreshold. The public place management platform 230 may also beconfigured to determine, based on the nodes of the knowledge map, aconnecting line between the nodes of the knowledge map as the edge ofthe graph input into the graph neural network model in response to thatthe distance between the nodes of the knowledge map is less than thesecond threshold and the count of hops between the nodes of theknowledge map is less than the third threshold.

In some embodiments, the edge characteristic of the edge of the graphinput into the graph neural network model may include a first transitionprobability characteristic. In some embodiments, the knowledge map mayinclude an edge with a reachable relationship, and the first transitionprobability characteristic may be determined through a second transitionprobability characteristic of an edge with a direct reachablerelationship. For more details about the determination of an edge of agraph input to the graph neural network model, refer to FIG. 6 and therelated descriptions, which is not repeated herein.

It should be noted that the description of the system and the componentsof the system may be only for convenience of description, which does notlimit the present disclosure within the scope of the embodiments. It maybe understood that for those skilled in the art, after understanding theprinciples of the system, various modules may be combined with eachother arbitrarily, or form a subsystem to connect with other moduleswithout departing from the principles. In some embodiments, the sensornetwork platform and the public place management platform may beintegrated in one component. For example, each module may share astorage module, and each module may also have its own storage module.Such deformations may be all within the scope of the present disclosure.

FIG. 3 is a schematic flowchart illustrating an IoT system for managinga public place in a smart city according to some embodiments of thepresent disclosure. As shown in FIG. 3 , a process 300 may include thefollowing operations. In some embodiments, the process 300 may beperformed by the public place management platform 230.

In operation 310, the public place management platform 230 may obtainpedestrian distribution information in a preset area during a currenttime period.

The preset area may refer to a preset area that needs to monitor thepedestrian distribution information and has a certain range. Forexample, the preset area may include one or more public places such as aparking lot area, a park area, a subway area, a bus area, a museum area,a stadium area, a shopping mall area, or the like.

The current time period may refer to a time period to which a currentmoment belongs. For example, if the current moment is 11:00, the currenttime period may be 10:40 to 11:00, 10:50 to 11:10, or the like.

The pedestrian distribution information may refer to differentdistribution information of users in a plurality of areas. For example,the pedestrian information may include different counts of userscorresponding to the plurality of areas, or the like. Merely as anexample, the count of users corresponding to a park area 1 may be 500,the count of users corresponding to a museum area 2 may be 1000, or thelike.

In some embodiments, the related data of the pedestrian distributioninformation may be represented in a form of a two-tuples. For example,the form of the two-tuples may be (A, A1), wherein A may represent thearea, and A1 may represent the count of users corresponding to the areaA. In the example mentioned above, the count of users corresponding tothe park area 1 is 500, and the corresponding two-tuples is (1, 500).The count of users corresponding to the museum area 2 is 1000, and thecorresponding two-tuples is (2, 1000).

In some embodiments, the pedestrian distribution information may berepresented by a plurality of population flow loads corresponding to aplurality of areas in a preset area. Different preset areas maycorrespond to different population flow loads. A plurality of presetareas may correspond to the plurality of population flow loads. Thepedestrian distribution information in the preset area may be determinedthrough the plurality of population flow loads. For the relevantdescriptions about the population flow load, refer to the relevantdescriptions in operation 320, which is not repeated herein.

In some embodiments, the public place management platform may obtain thepedestrian distribution information in the preset area during thecurrent time period from one or more components in the applicationscenario 100 or an external device. For example, the public placemanagement platform may obtain the population flow load of the pluralityof public places from the management platform database. The pedestriandistribution information may be obtained by calculating (e.g., manualcalculation, etc.) the population flow load of the plurality of publicplaces.

In some embodiments, the public place management platform may obtain thepedestrian distribution information through the management platformdatabase. In some embodiments, the public place management platform mayobtain data of the management platform database through one or moremanagement sub-platform databases.

In some embodiments, the public place management platform 230 mayinclude a corresponding management platform database 231 shown in FIG. 4. The management platform database 231 may store the data of the publicplace management platform and obtain the data of the managementsub-platform database. For example, the management platform database 231may store the data of the public place management platform (e.g., thepedestrian distribution information, etc.). The management platformdatabase 231 may obtain the data of the management sub-platform database233.

In some embodiments, the management sub-platform 232 may include acorresponding management sub-platform database 233. As shown in FIG. 4 ,the management sub-platform database 233 may include a parking lotmanagement platform database 2331, a park management platform database2332, a subway management platform database 2333, a bus managementplatform database 2334, a museum management platform database 2335, astadium management platform database 2336, a shopping mall managementplatform database 2337, or the like. For more details about themanagement sub-platform 232 and the management sub-platform database233, refer to FIG. 4 and the related descriptions, which is not repeatedherein.

In some embodiments, the public place management platform 230 may obtainthe pedestrian distribution information through the management platformdatabase 231. For example, the public place management platform mayobtain a plurality of data of the current time period in the managementplatform database. The public place management platform may analyze andprocess the plurality of data to obtain the pedestrian distributioninformation of one or more public places.

In some embodiments, the management platform database may obtain datathrough one or more management sub-platform databases. For example, themanagement platform database may obtain data of the parking lotmanagement platform database, the park management platform database, thesubway management platform database, the shopping mall managementplatform database, or the like, respectively. In some embodiments, themanagement platform database may periodically obtain data from theplurality of management sub-platform databases based on a certain presetrule. For example, the preset rule may be an interval for storing data,and the interval may be 10 minutes, 30 minutes, or the like. Themanagement platform database may obtain data from the plurality ofmanagement sub-platform databases every 30 minutes.

In some embodiments, the pedestrian distribution information may beobtained through the management platform database. The managementplatform database may obtain data through the one or more managementsub-platform databases, and obtain data from the management sub-platformdatabases corresponding to the plurality of public places. Thecomprehensiveness of the pedestrian distribution information may beensured, which provides a guarantee for the determination of thepopulation flow load of different areas in the future time period.

In operation 320, the public place management platform may determine atleast one area location in a preset area for a future time period basedon the pedestrian distribution information.

The future time period may refer to a period of time after a currentmoment. For example, if the current moment is 11:00, the future timeperiod may be 11:00 to 12:00, 11:30-11:12:30, or the like.

The area location may refer to a location where the population flow loadcorresponding to part or all of the preset area is greater than a firstthreshold. The area location may be a location corresponding to one ormore areas in the preset area or a location corresponding to one or morepartial areas in a certain area. For example, the area location may be alocation corresponding to a museum area, a location corresponding to abasketball court in a stadium area, a location corresponding to a soccerfield, or the like. The population flow loads of the correspondingpositions mentioned above may all be greater than the first threshold.

In some embodiments, the population flow load of the area location maybe greater than the first threshold. The population flow load may referto a carrying capacity of users at one or more areas in a certain periodof time. For example, in the future time period of 11:00 to 12:00, thepopulation flow load of the location corresponding to the museum areamay be 1000. The first threshold may refer to a preset value of thepopulation flow load of the area corresponding to the area location. Thefirst thresholds corresponding to different area locations may be thesame or different. In some embodiments, the first threshold may be setbased on experience or actual needs. For example, the first thresholdmay be set according to an area size corresponding to the area location.

For example, a region area corresponding to the area location 1 may be5000 square meters, and the first threshold may be 700. A region areacorresponding to the area location 2 may be 10,000 square meters, andthe first threshold may be 1,400. For another example, the firstthreshold may be set according to an area property corresponding to thearea location. For example, the first thresholds corresponding todifferent area locations with the same region area may be different. Thefirst threshold corresponding to the location of the museum may be 300,and the first threshold corresponding to the location of the basketballcourt in the stadium may be 800.

In some embodiments, the public place management platform may determineone or more area locations in a preset area for a future time periodbased on the pedestrian distribution information. For example, thepublic place management platform 230 may predict the population flowload at different locations of one or more preset areas in the futuretime period based on the pedestrian distribution information during thecurrent time period. The public place management platform 230 maydetermine the area location according to the population flow loadscorresponding to different locations of different preset areas. Forexample, when the population flow loads corresponding to three locationsin the preset area are greater than the corresponding first thresholds,respectively, the public place management platform 230 may determine thethree locations in the preset area as the area locations.

In operation 330, the public place management platform may determineprompt information based on the area location.

The prompt information may refer to information prompting the user aboutthe one or more area locations. In some embodiments, the promptinformation may be a preset template. For example, a template 1 may be“Hello everyone, the population flow of an area location is large at themoment, please do not go there.” A template 2 may be “the populationflow of an area location is relatively large, please try to choose otherplaces.” A template 3 may be “please protect your personal belongings,”or the like.

In some embodiments, the public place management platform may determinethe prompt information based on the area location. For example, the arealocation may be a location corresponding to the basketball court in thestadium, and the prompt information determined by the public placemanagement platform may be “Hello everyone, the population flow of thebasketball court in the stadium is large now, please do not go there.”

In operation 340, the public place management platform may send theprompt information to the user platform through the service platform.

In some embodiments, the public place management platform may send theprompt information to the user platform through the service platform.For example, the public place management platform may send the promptinformation of “Hello everyone, the population flow of the basketballcourt in the stadium is large now, please do not go there” to the userplatform through the service platform.

In some embodiments, the area location with a larger population flow inthe future time period may be determined based on the pedestriandistribution information in the preset area during the current timeperiod, and the prompt information may be sent to the user. The user maychoose which areas to go to according to the prompt information. Theuser may not go to areas with large population flow. Gathering of peoplemay be avoided, which reduces the time for users to queue or wait, andsaves time for users.

FIG. 4 is a schematic diagram illustrating a public place managementplatform, a sensor network platform, and an object platform according tosome embodiments of the present disclosure.

In some embodiments, the at least one management sub-platform databasemay correspond to at least one management sub-platform. The at least onemanagement sub-platform may include at least one of a parking lotmanagement platform 2321, a park management platform 2322, a subwaymanagement platform 2323, a bus management platform 2324, a museummanagement platform 2325, a stadium management platform 2326, or ashopping mall management platform 2327.

The management sub-platform 232 may refer to a platform for managing acertain public place. Different management sub-platforms may managedifferent public places. For example, the parking lot managementplatform 2321 may manage parking lots of a public place, and the museummanagement platform 2325 may manage a museum of a public place.

In some embodiments, the at least one management sub-platform 232 mayobtain data through the corresponding at least one managementsub-platform database 233. For example, the parking lot managementplatform 2321 may obtain the population flow load during the currenttime period from the parking lot management platform database 2331. Asanother example, the subway management platform 2323 may obtain thepopulation flow load during the current time period from the subwaymanagement platform database 2333.

In some embodiments, the plurality of management sub-platform databases233 may obtain data from the object platform 250 through the sensornetwork platform database.

The sensor network platform 240 may include a sensor network platformdatabase 2401, and the sensor network platform may realize datainteraction with the public place management platform and the objectplatform through the sensor network platform database 2401. The sensornetwork platform 240 may include a plurality of sensor networksub-platforms, such as a parking lot sensor network platform, a parksensor network platform, a subway sensor network platform, a bus sensornetwork platform, a museum sensor network platform, a stadium sensornetwork platform, a shopping mall sensor network platform, or the like.The sensor network sub-platform may extract data from the sensor networkplatform database 2401, perform calculation processing on thecorresponding data, and give feedback of the processing results to thesensor network platform database 2401.

In some embodiments, the object platform 250 may include a plurality ofdevices, for example, at least one of a ticket checking device 2501, acamera monitoring device 2502, an unmanned aerial vehicle (UAV) device2503, or the like. The ticket checking device 2501 may refer to a devicefor checking and identifying relevant information. The count of userspassing through a preset area may be determined through the ticketchecking device 2501. A plurality of camera monitoring devices 2502 andUVA devices 2503 may monitor and take pictures of a plurality of presetareas. The population flow information of the plurality of preset areasmay be calculated based on the video captured by the camera monitoringdevice and the UAV device. In some embodiments, the UVA device 2503 maycarry an infrared sensor. The public place management platform mayobtain a heat map of the pedestrian distribution information of theplurality of preset areas based on the infrared sensor.

In some embodiments, the different management sub-platform databases 233may obtain data from the object platform 250 (e.g., the ticket checkingdevice 2501, etc.) through the sensor network platform database. Forexample, the management sub-platform database 233 may obtain the countof users passing through a certain preset area from a counter of theticket checking device 2501 through the sensor network platformdatabase. As another example, the management sub-platform database 233may obtain data from the camera monitoring device 2502 through thesensor network platform database. For example, the camera monitoringdevice 2502 may shoot a plurality of videos, and the managementsub-platform database 233 may obtain the plurality of videos through thesensor network platform database.

In some embodiments, the sensor network sub-platform may process theplurality of videos based on a recognition model, and obtain thepopulation flow information of the plurality of preset areas in thevideos. The recognition model may refer to a model that recognizes auser in a video. In some embodiments, a type of the recognition modelmay include a Yolo model or the like. In some embodiments, the input ofthe recognition model may include each frame of the video, or the like.The output of the recognition model may include an image segmentationresult. The image segmentation result may include a plurality of objectboxes and categories corresponding to the object boxes. For example, oneobject box may correspond to one user, and the plurality of object boxesmay correspond to the plurality of users. A video may correspond to theplurality of object boxes, and the plurality of object boxes of thevideo may be designated as a group of object boxes.

In some embodiments, the recognition model may be obtained by trainingbased on a plurality of training samples and labels.

In some embodiments, the training samples may include sample videos. Alabel may be a sample object from a user and a category corresponding tothe object box. Training data (e.g., training samples) may be obtainedbased on historical surveillance videos, and the labels of the trainingdata may be determined by manual labeling or automatic labeling. Thetraining samples with labels may be input into an initial recognitionmodel. Parameters of the initial recognition model may be updatedthrough training. When the trained initial recognition model satisfies apreset condition, the training may end, and the trained recognitionmodel may be obtained.

In some embodiments, images of different boxes may include the sameuser. The categories corresponding to the plurality of object boxesoutput by the recognition model may correspond to the same user. Thesensor network sub-platform may determine whether the same user existsin the plurality of object boxes of a group of object boxes based on anobject determination model. When the same user exists in the pluralityof object boxes, the same object boxes may be merged. When the sameobject boxes in a group of object boxes are merged, the count of objectboxes in an existing group of object boxes may be determined as thecount of users in the video.

In some embodiments, types of the object determination model may includea CNN model, a DNN model, or the like. The sensor network sub-platformmay input an object box into the CNN model for image feature extraction,and obtain an image feature corresponding to the object box. A pluralityof image features corresponding to a group of object boxes may beobtained by inputting the group of object boxes into the CNN model forimage feature extraction.

In some embodiments, the input of the CNN model may include theplurality of object boxes. The output of the CNN model may include imagefeatures corresponding to different object boxes, and different imagefeatures may be represented by a feature vector. The sensor networksub-platform may input two image features into the DNN model todetermine whether the two image features correspond to the same user. Insome embodiments, the input of the DNN model may include two imagefeatures. The output of the DNN model may include a result of whetherthe two image features correspond to the same user. For example, theoutput result of “Yes” may indicate the image features correspond to thesame user, and the output result of “No” may indicate the image featuresdo not correspond to the same user.

In some embodiments, the CNN model and the DNN model may be obtainedthrough joint training based on the plurality of training samples andlabels.

In some embodiments, the training samples may include a sample group ofobject boxes. A label may be whether the sample object box correspondsto the same user. The training data may be obtained based on historicaldata, and the labels of the training data may be determined by manuallabeling or automatic labeling. For example, a sample label thatcorresponds to the same user may be marked as 1 in a sample object box,and a sample label that does not correspond to the same user may bemarked as 0 in a sample object box. The training samples with labels maybe input into an initial CNN model and an initial DNN model. Parametersof the initial CNN model and the initial DNN model may be updated bytraining. When the trained initial CNN model and the initial DNN modelsatisfy a preset condition, the training may end, and the trained CNNmodel and DNN model may be obtained.

In some embodiments, the sensor network sub-platform may calculate theplurality of object boxes in the group of object boxes based on afeature extraction algorithm and a feature similarity algorithm, anddetermine whether the same user exists in object boxes. For example, thesensor network sub-platform may perform feature extraction on two of theplurality of object boxes of the group of object boxes through thefeature extraction algorithm (e.g., the HOG algorithm), and obtain afeature vector of each object box. The sensor network sub-platform maydetermine whether each object box corresponds to the same user based onthe similarity (e.g., a Euclidean distance, etc.) between the featurevector of each object box. When there is the same user, the same objectboxes may be merged, and after merging, the count of object boxes in thegroup of object boxes may be determined as the count of users in thevideo. In some embodiments, the sensor network sub-platform may alsodetermine whether the same user exists in the object boxes in otherways, which is not limited in the present disclosure.

In some embodiments, data of the plurality of management sub-platformdatabases may be obtained from the plurality of object platforms throughthe sensor network platform database, which ensures the diversity andaccuracy of the data of the management sub-platform database, therebyensuring the accuracy of the obtained population flow information. Insome embodiments, the object boxes of the same user may be merged bydetermining the count of users in the video based on the recognitionmodel and the object determination model. Thus, the accuracy of thecount of users can be further ensured, thereby ensuring the accuracy ofthe data of the management sub-platform database.

In some embodiments, the subway management platform databasecorresponding to the subway management platform may obtain data from theticket checking gate through the sensor network platform database.

In some embodiments, the sensor network platform database 2401 may beconnected to the subway management platform database 2333 and the ticketchecking gate to realize the functions of sensing communication ofperception information and control information.

The ticket checking gate may refer to a device for checking andidentifying information of users taking the subway. The population flowinformation of a plurality of subway entrances and exits may bedetermined based on a plurality of ticket checking gates.

In some embodiments, the subway management platform database 2333 mayobtain data from the ticket checking gate through the sensor networkplatform database 2401. For example, the subway management platformdatabase 2333 may obtain the population flow information of theplurality of subway entrances and exits from the counter of the ticketchecking gate through the sensor network platform database 2401.

In some embodiments, the subway management platform database may obtaindata from the ticket checking gate through the sensor network platformdatabase, which ensures the accuracy of the data in the database of thesubway management platform, thereby ensuring the accuracy of thepopulation flow information of the subway in a public place.

FIG. 5 is a schematic diagram illustrating a determination of an arealocation based on pedestrian distribution information according to someembodiments of the present disclosure. In some embodiments, a process500 may be performed by the public place management platform 230.

In some embodiments, the public place management platform may determineat least one area location based on a processing of the pedestriandistribution information through an area location prediction model.

The area location prediction model 520 may refer to a model fordetermining an area location.

Merely as an example, the pedestrian distribution information 510 may beinput into the area location prediction model 520, and the area locationprediction model 520 may output one or more area locations 530. In someembodiments, a plurality of areas in the preset area and a plurality ofpopulation flow loads corresponding to the plurality of areas,respectively, may be input into the area location prediction model. Thearea location prediction model may output one or more area locations530.

In some embodiments, the public place management platform 230 may obtainthe area location prediction model from one or more components in theapplication scenario 100 (e.g., the storage device 130, the terminal140) or an external device via the network (e.g., the network 120). Forexample, the area location prediction model may be trained by acomputing device (e.g., the processing device 110) and stored in astorage device (e.g., the storage device 130) in the applicationscenario 100. The processing device 110 may access the storage deviceand retrieve the area location prediction model.

In some embodiments, the public place management platform may train thearea location prediction model based on at least one training sample anda label. In some embodiments, the area location prediction model may betrained by the public place management platform according to a machinelearning algorithm, and the public place management platform may obtainat least one training sample. Each training sample may include samplepedestrian distribution information. A label may be a sample arealocation. In some embodiments, the sample area location may be markedmanually or by the public place management platform. The training datamay be obtained based on historical data, and the label of the trainingdata may be determined by manual labeling or automatic labeling. Forexample, the sample area location may be determined by the actualpopulation flow load of the preset area in the sample pedestriandistribution information. The public place management platform maydesignate a corresponding area location that the actual population flowload of the preset area in the sample pedestrian distributioninformation is greater than a first threshold as the sample arealocation.

In some embodiments, the area location prediction model 520 may includea graph neural network (GNN) model 525 or the like.

The GNN model may refer to a model used to determine the area location.

The input of the GNN model may be a graph. The graph may include nodes521 and edges 523. The output of the GNN model may be determined basedon the correlation of each node in the graph composed of nodes andedges. The output of the GNN model may be an area location 530 that thepopulation flow load is greater than the first threshold.

In some embodiments, the public place management platform may train theGNN model based on at least one training sample and a label. In someembodiments, the GNN model may be trained by the public place managementplatform based on a machine learning algorithm. The public placemanagement platform may obtain at least one training sample. Eachtraining sample may include sample pedestrian distribution information.A label may be a sample area location. In some embodiments, the samplearea location may be marked manually or by the public place managementplatform. The training data may be obtained based on the historical dataof a plurality of management sub-platforms, and the label of thetraining data may be determined by manual labeling. For example, amaximum threshold of the population flow load of the plurality of publicplaces may be determined, and the locations of one or more public placesdetermined by manual labeling may be determined as the sample arealocation.

In the GNN model, each public place site may be designated as the node521 of the graph input into the model, and the relationship between eachpublic place site may be designated as the edge of the graph input intothe model.

A public place site may refer to a place that may provide services tousers, for example, a parking lot, a park, a subway station, a bus stop,a shopping mall, or the like.

In some embodiments, a node characteristic 522 input into the GNN modelmay be an information characteristic corresponding to at least onepublic place. In some embodiments, the node characteristic 522 mayinclude a characteristic of at least one public place site such as thecount of entrances and exits, distribution locations of the entrancesand the exits, time information, node environment information, holidayinformation, a parking lot size, or the like.

In some embodiments, a surrounding traffic site of each public placesite may also be designated as a node of the graph input into the GNNmodel, and the node characteristic may also include surrounding trafficflow. The surrounding traffic flow may refer to traffic information suchas the count of people entering or exiting in a time period of asurrounding traffic node, the count of users at the current moment, orthe like. For example, a subway station a and a bus station b may be thesurrounding traffic nodes of the “shopping mall A”. The surroundingtraffic flow of the node characteristic of the shopping mall A mayinclude the count of people entering or exiting in a time period of thesubway station a and the bus station b, the count of users at thecurrent moment, or other flow information.

In some embodiments, the public place management platform may obtain aplurality of related traffic nodes corresponding to one or more publicplace sites in a knowledge map. The surrounding traffic flow of a publicplace site may be determined by the flow information of the relevanttraffic node. For example, the node may be the public site of theshopping mall A, and the relationship with the shopping mall A in theknowledge map may be that “shopping mall A arrives at Metro Line 1” or“shopping mall A arrives at Metro Line 3”. The public place managementplatform may designate Metro Line 1 and Metro Line 3 as the surroundingtraffic nodes of the shopping mall A. The public place managementplatform may determine the flow information of Metro Line 1 and MetroLine 3 as the surrounding traffic flow of the shopping mall A.

In some embodiments, an edge characteristic 524 of the edge input intothe GNN model may be a relationship between two connected nodes, forexample, a relationship between two public place sites. The edgecharacteristic of the edge input into the GNN model may include arelationship strength vector. The relationship strength vector mayreflect the relationship strength between two nodes.

In some embodiments, the public place management platform may determinethe relationship strength vector from a plurality of vectors. Forexample, the public place management platform may determine therelationship strength vector by weighting or splicing the plurality ofvectors.

In some embodiments, the plurality of vectors may include distancestrength vectors, location strength vectors, and category strengthvectors. In some embodiments, the distance strength vector may representa distance between two nodes connected by an edge. The public placemanagement platform may determine the distance strength vector by thedistance between two public places corresponding to two nodes connectedby a certain edge. The smaller the distance between two public places,the greater the corresponding distance strength vector.

In some embodiments, the location strength vector may represent whetheran arrival route between two nodes connected by a certain edge isconvenient. The public place management platform may determine thelocation strength vector based on whether the arrival route between twopublic places corresponding to two nodes connected by a certain edge isconvenient. For example, if node B is easy to reach from node A withoutavoiding obstacles, the location strength vector of the edgecharacteristic corresponding to the edge from node A to node B may berelatively large. As another example, if node D is not easy to reachfrom node C since several obstacles need to be avoided, the locationstrength vector of the edge characteristic corresponding to the edgefrom node C to node D may be relatively small.

In some embodiments, the category strength vector may represent thesimilarity between two nodes connected by an edge. The public placemanagement platform may determine the category strength vector throughthe categories of the two public places corresponding to the two nodesconnected by a certain edge. For example, if node E and node F are bothshopping malls, the category strength vector of the edge characteristicof the edge corresponding to node E and node F may be relatively large,which may be an upper limit value (e.g., 1). As another example, if thecategories of node G and node H are completely irrelevant (e.g., a parkand a museum), the category strength vector of the edge characteristicof the edge corresponding to node G and node H may be small, which maybe a lower limit value (e.g., 0). The illustrations mentioned above areonly for the convenience of description, and the related distancestrength vector, location strength vector, and category strength vectormay be vectors with more than one dimension. For example, the locationstrength vector may be a three-dimensional vector, which represents “isit easy to reach by walking”, “is it easy to reach by car”, or “is iteasy to reach by subway”.

In some embodiments, the public place management platform may processthe pedestrian distribution information based on the area locationprediction model to determine one or more area locations. For example,the public place management platform may input the pedestriandistribution information 510 into the area location prediction model.The area location prediction model may output the one or more arealocations with a population flow load greater than the first threshold.As another example, the processing device may input the nodecharacteristic and edge characteristic into the GNN model, and the GNNmodel may output the one or more area locations with the population flowload greater than the first threshold.

In some embodiments, the one or more area locations may be determined byprocessing the pedestrian distribution information through the arealocation prediction model, which improves the accuracy of determiningthe area location with greater population flow load in the future timeperiod.

FIG. 6 is a schematic flowchart illustrating a determination of an edgeof a graph input into a graph neural network model according to someembodiments of the present disclosure. In some embodiments, a process600 may be performed by the public place management platform 230.

In operation 610, the public place management platform may obtain adistance between nodes of the knowledge map and a count of hops betweenthe nodes of the knowledge map based on the knowledge map.

The knowledge map may reflect the areas, locations, and relationships ofa plurality of public places. In some embodiments, the knowledge map mayinclude nodes and relationships. The relationship of the knowledge mapmay refer to the relationship between nodes. The nodes may include theplurality of public places, transportation nodes, or the like. Theplurality of public places may include shopping mall A, a park, aplayground, or the like. The traffic nodes may include a subway station,a bus station, entrances and exits of a certain road, or the like. Forexample, station A of subway line 1, bus station B, the exit or entranceof road C, or the like. In some embodiments, attributes of the nodes maybe represented by the population flow. Different nodes in the knowledgemap may correspond to different population flows.

In some embodiments, a relationship between nodes may include areachable relationship, or the like. The reachable relationship mayrefer to that one node may be reached from another node. The reachablerelationship may include a direct reachable relationship, a non-directreachable relationship, or the like. For example, the nodes are shoppingmall A and subway line 1 in a public place, and the relationship betweenthe shopping mall A and the subway line 1 may be “subway line 1 isreachable from shopping mall A”. Therefore, the relationship between theshopping mall A and the subway line 1 may be the direct reachablerelationship. As another example, the nodes are shopping mall C andshopping mall D in a public place, and the relationship between theshopping mall C and the shopping mall D may be “subway line 1 isreachable from shopping mall C” and “shopping mall D is reachable fromsubway line 1”. Therefore, the relationship between the shopping mall Cand the shopping mall D may be the non-direct reachable relationship.

In some embodiments, the relationship between nodes may include adistance between nodes, a transition probability between nodes, or thelike. The distance between nodes may reflect whether a location of onenode is far from a location of another node or not. For example, thedistance between the nodes may be 500 meters, 1 kilometer, 3 kilometers,or the like.

The transition probability between nodes may reflect the probability ofa user from one node to another node. For example, in the knowledge map,the shopping mall A may have two reachable relationships. Relationship 1may be “station A of subway line 1 is reachable from shopping mall A”.Relationship 2 may be “station F of subway line 3 is reachable fromshopping mall A”. The transition probabilities of the relationship 1 andthe relationship 2 may be 40% and 60%, respectively, which indicatesthat the probability of the user of the shopping mall A going to thestation A of subway line 1 may be 40% and the probability of the user ofthe shopping mall A going to the station F of subway line 3 may be 60%.In some embodiments, the transition probability between the nodes may bedetermined based on the manual setting.

In some embodiments, the count of hops between nodes may reflect therelationship between two nodes. The relationship between the two nodesmay be the direct reachable relationship, the non-direct reachablerelationship, or the like. For example, “node-edge-node” may bespecified as one hop, wherein the edge may represent the relationshipbetween the nodes. For example, the relationship between the shoppingmall A and the shopping mall B in a public place may be direct reachablerelationship (e.g., “shopping mall B is reachable from shopping mallA”), that is, one hop from the shopping mall A to the shopping mall B.As another example, the relationship between the shopping mall C and theshopping mall D in a public place may not be direct reachablerelationship, and the relationship between the shopping mall C and theshopping D may be “subway line 1 is reachable from shopping mall C” and“shopping mall D is reachable from subway line 1”, that is, two hopsfrom the shopping mall C to the shopping mall D.

In some embodiments, the public place management platform may obtain adistance between nodes of the knowledge map and a count of hops betweenthe nodes of the knowledge map based on the knowledge map. For example,the public place management platform may obtain the distance betweennodes of the knowledge map and the count of hops between nodes of theknowledge map from one or more components in the application scenario100 or an external device.

In operation 620, the public place management platform may determinewhether the distance between the nodes of the knowledge map is less thana second threshold and whether the count of hops between the nodes ofthe knowledge map is less than a third threshold.

The second threshold may refer to a preset value of the distance betweennodes. For example, the second threshold may be a maximum value of thedistance between nodes (e.g., 2 kilometers). When the distance betweentwo nodes is less than 2 kilometers, a connecting line between thecorresponding two nodes may be the edge of the graph input into thegraph neural network model.

The third threshold may refer to a preset value of the count of hopsbetween nodes. For example, the third threshold may be a maximum countof hops between nodes (e.g., two hops). When the count of hops betweentwo nodes is less than two hops, a connecting line between thecorresponding two nodes may be the edge of the graph input into thegraph neural network model.

In some embodiments, the public place management platform may determinethe category of nodes by comparing the distance between every two nodesin the knowledge map with the second threshold. The public placemanagement platform may determine a plurality of node groups. Each ofthe plurality of node groups may include two nodes. The plurality ofnode groups that the distance between the two nodes is less than thesecond threshold may be designated as category 1. The public placemanagement platform may determine the category of nodes by comparing thecount of hops between every two nodes in the knowledge map with thethird threshold. The public place management platform may determine aplurality of node groups. Each of the plurality of node groups mayinclude two nodes and the count of hops between the two nodes in thenode group may be less than the third threshold. The plurality of nodegroups that the count of hops between the two nodes is less than thethird threshold may be designated as category 2.

In operation 630, in response to that the distance between the nodes ofthe knowledge map is less than the second threshold and the count ofhops between the nodes of the knowledge map is less than the thirdthreshold, the public place management platform may determine aconnecting line between the nodes of the knowledge map as the edge ofthe graph input into the graph neural network model based on the nodesof the knowledge map.

In some embodiments, the public place management platform may determinethe nodes that the distance between the nodes of the knowledge map isless than the second threshold, and the nodes that the count of hopsbetween the nodes of the knowledge map is less than the third threshold.The public place management platform may determine the connecting linebetween the nodes mentioned above as the edge of the graph input intothe graph neural network model. For example, as described in theembodiments mentioned above, the public place management platform maydetermine the connecting lines between the two nodes in each of theplurality of node groups that exist in the category 1 and the category 2simultaneously as the edges of the graph input into the graph neuralnetwork model.

In operation 640, when the distance between the nodes of the knowledgemap and the count of hops between the nodes of the knowledge map may notbe smaller than the threshold simultaneously, the public placemanagement platform may not perform operations.

In some embodiments, the connecting line between nodes that satisfiesthe second threshold and the third threshold may be determined as theedges of the graph input into the graph neural network model based onthe distance between the nodes of the knowledge map and the count ofhops between the nodes of the knowledge map, which ensures the accuracyof the edge of the graph input into the graph neural network model,thereby ensuring the accuracy of determining the area location.

In some embodiments, the edge characteristic of the edge of the graphinput into the GNN model may include a first transition probabilitycharacteristic.

The first transition probability characteristic may reflect apossibility that a node of an edge is reachable from the other node ofthe edge. For example, park 1 may have two reachable relationships.Relationship 3 may be “station G of subway line 1 is reachable from park1”. Relationship 4 may be “station H of subway line 3 is reachable frompark 1”. The first transition probabilities of the relationship 3 andthe relationship 4 may be 40% and 60%, respectively, which indicatesthat the probability of a user of the park 1 going to station G ofsubway line 1 may be 40% and the probability of a user of the park 1going to station H of subway line 3 may be 60%.

In some embodiments, the first transition probability characteristic maybe determined through a second transition probability characteristic ofan edge with a direct reachable relationship.

The second transition probability characteristic may reflect apossibility that a node corresponding to an edge in the knowledge map isreachable from another node.

In some embodiments, the public place management platform may determinethe first transition probability through the second transitionprobability of the edge that the relationship in the knowledge may bereachable. For example, the relationship in the knowledge map and thecorresponding second transition probability may be “subway line 1 isreachable from museum A, and the corresponding second transitionprobability is 40%.” Shopping mall B is reachable from subway line 1,and the corresponding second transition probability is 40%. The edge ofthe graph input into the graph neural network model may be “shoppingmall B is reachable from museum A.” The museum A may only reach theshopping mall B through the subway line 1. The first transitionprobability corresponding to the edge of the graph in the graph neuralnetwork model may be 16% (40%×40%=16%), wherein the edge may represent“shopping mall B is reachable from museum A”.

In some embodiments, the first transition probability characteristic ofthe edge of the graph input into the graph neural network model may bedetermined through the second transition probability characteristic ofthe edge with the direct reachable relationship in the knowledge map,which ensures the accuracy of the first transition probabilitycharacteristic, thereby further ensuring the accuracy of determining thearea location.

It should be noted that the above descriptions are merely provided forthe purposes of illustration, and not intended to limit the scope of thepresent disclosure. For persons having ordinary skills in the art,multiple variations and modifications may be made under the teachings ofthe present disclosure. However, those variations and modifications donot depart from the scope of the present disclosure.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure, or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures, or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or collocation of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code, etc.) or combiningsoftware and hardware implementation that may all generally be referredto herein as a “unit,” “module,” or “system.” Furthermore, aspects ofthe present disclosure may take the form of a computer program productembodied in one or more computer readable media having computer-readableprogram code embodied thereon.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various embodiments. This method ofdisclosure, however, is not to be interpreted as reflecting an intentionthat the claimed subject matter requires more features than areexpressly recited in each claim. Rather, claimed subject matter may liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, numbers describing the number of ingredients andattributes are used. It should be understood that such numbers used forthe description of the embodiments use the modifier “about”,“approximately”, or “substantially” in some examples. Unless otherwisestated, “about”, “approximately”, or “substantially” indicates that thenumber is allowed to vary by ±20%. Correspondingly, in some embodiments,the numerical parameters used in the description and claims areapproximate values, and the approximate values may be changed accordingto the required characteristics of individual embodiments. In someembodiments, the numerical parameters should consider the prescribedeffective digits and adopt the method of general digit retention.Although the numerical ranges and parameters used to confirm the breadthof the range in some embodiments of the present disclosure areapproximate values, in specific embodiments, settings of such numericalvalues are as accurate as possible within a feasible range.

For each patent, patent application, patent application publication, orother materials cited in the present disclosure, such as articles,books, specifications, publications, documents, or the like, the entirecontents of which are hereby incorporated into the present disclosure asa reference. The application history documents that are inconsistent orconflict with the content of the present disclosure are excluded, andthe documents that restrict the broadest scope of the claims of thepresent disclosure (currently or later attached to the presentdisclosure) are also excluded. It should be noted that if there is anyinconsistency or conflict between the description, definition, and/oruse of terms in the auxiliary materials of the present disclosure andthe content of the present disclosure, the description, definition,and/or use of terms in the present disclosure is subject to the presentdisclosure.

Finally, it should be understood that the embodiments described in thepresent disclosure are only used to illustrate the principles of theembodiments of the present disclosure. Other variations may also fallwithin the scope of the present disclosure. Therefore, as an example andnot a limitation, alternative configurations of the embodiments of thepresent disclosure may be regarded as consistent with the teaching ofthe present disclosure. Accordingly, the embodiments of the presentdisclosure are not limited to the embodiments introduced and describedin the present disclosure explicitly.

What is claimed is:
 1. A method for managing a public place in a smartcity, which is executed by a processor of at least one public placemanagement platform, comprising: obtaining pedestrian distributioninformation in a preset area during a current time period via networkfrom a storage device, including: a processor of a sensor networksub-platform processing a plurality of videos based on a recognitionmodel; obtaining population flow information of a plurality of presetareas in the plurality of videos, wherein the recognition model includesa Yolo model that recognizes users in the plurality of videos and therecognition model is obtained through a training process, comprising:generating a plurality of first training samples and first labels;wherein the plurality of first training samples include sample videosobtained based on historical surveillance videos, the first labelsinclude a sample object from the users and a category corresponding toan object box; inputting the plurality of first training samples withlabels into an initial recognition model; updating parameters of theinitial recognition model through training; and obtaining therecognition model when the initial recognition model satisfies a presetcondition; obtaining pedestrian distribution information in the presetarea during a current time period based on the population flowinformation; and receiving the pedestrian distribution informationtransmitted from the sensor network sub-platform; generating, based onthe pedestrian distribution information, at least one area location inthe preset area for a future time period, a population flow load of thearea location being greater than a first threshold, wherein the at leastone area location is determined by processing the pedestriandistribution information through an area location prediction model, thearea location prediction model includes a graph neural network model,and the area location prediction model is trained by the public placemanagement platform according to a machine learning algorithm based onat least one second training sample and at least one second label,wherein each second training sample includes sample pedestriandistribution information, and each second label is a sample arealocation that is determined by an actual population flow load of thepreset area in the sample pedestrian distribution information, whereingenerating the sample area location includes: designating, by theprocessor of the public place management platform, a corresponding arealocation that the actual population flow load of the preset area in thesample pedestrian distribution information is greater than the firstthreshold as the sample area location; a graph input into the graphneural network model includes at least two nodes and at least one edge,each of the at least two nodes includes at least one public place site,and the at least one edge includes a relationship between the at leasttwo nodes; a node characteristic of each node includes a count ofentrances and exits of the at least one public place site, distributionpositions of the entrances and the exits, time information, nodeenvironment information, holiday information, or a size of a parkinglot; an edge characteristic of the at least one edge includes arelationship strength vector, wherein the relationship strength vectorreflects a relationship strength between two nodes, and the relationshipstrength vector is determined from a plurality of vectors, wherein theplurality of vectors include distance strength vectors, locationstrength vectors, and category strength vectors; the node characteristicincludes surrounding traffic flow; an output of the graph neural networkmodel is the area location, and the area location is determined based ona correlation of each node in the graph composed of nodes and edges;generating, based on the area location, prompt information; andfeedbacking, the prompt information to a user terminal of a userplatform through a service platform via network.
 2. The method of claim1, wherein the obtaining pedestrian distribution information in a presetarea during a current time period includes: obtaining the pedestriandistribution information through a management platform database, themanagement platform database obtaining data through at least onemanagement sub-platform database.
 3. The method of claim 2, wherein theat least one management sub-platform database corresponds to at leastone management sub-platform, and the at least one managementsub-platform includes: at least one of a parking lot managementplatforms, a park management platform, a subway management platform, abus management platform, a museum management platform, a stadiummanagement platform, or a shopping mall management platform; and the atleast one management sub-platform obtains data through the correspondingat least one management sub-platform database.
 4. The method of claim 3,wherein the at least one management sub-platform database obtains datafrom an object platform through a sensor network platform database, andthe object platform includes at least one of a ticket checking device, acamera monitoring device, or an unmanned aerial vehicle (UAV) device. 5.The method of claim 4, wherein a subway management platform databasecorresponding to the subway management platform obtains data from theticket checking device through the sensor network platform database. 6.The method of claim 1, wherein the at least one edge of the graph inputinto the graph neural network model is determined by a processincluding: obtaining, based on a knowledge map, a distance between nodesof the knowledge map and a count of hops between the nodes of theknowledge map; determining whether the distance between the nodes of theknowledge map is less than a second threshold and whether the count ofhops between the nodes of the knowledge map is less than a thirdthreshold; in response to determining that the distance between thenodes of the knowledge map is less than the second threshold and thecount of hops between the nodes of the knowledge map is less than thethird threshold, determining, based on the nodes of the knowledge map, aconnecting line between the nodes of the knowledge map as the edge ofthe graph input into the graph neural network model.
 7. The method ofclaim 6, wherein the edge characteristic of the edge of the graph inputinto the graph neural network model includes a first transitionprobability characteristic, the knowledge map includes an edge with areachable relationship, and the first transition probabilitycharacteristic is determined by a second transition probabilitycharacteristic of an edge with a direct reachable relationship.
 8. AnInternet of Things system for managing a public place in a smart city,comprising a user platform, a service platform, a public placemanagement platform, a sensor network platform, and an object platform,the management platform of the public place includes a managementsub-platform, a management sub-platform database, wherein the publicplace management platform is configured to perform the followingoperations including: obtaining pedestrian distribution information in apreset area during a current time period via network from a storagedevice, including: a processor of a sensor network sub-platform of thesensor network platform processing a plurality of videos based on arecognition model; obtaining population flow information of a pluralityof preset areas in the plurality of videos, wherein the recognitionmodel includes a Yolo model that recognizes users in the plurality ofvideos and the recognition model is obtained through a training process,comprising: generating a plurality of first training samples and firstlabels; wherein the plurality of first training samples include samplevideos obtained based on historical surveillance videos, the firstlabels include a sample object from the users and a categorycorresponding to an object box; inputting the plurality of firsttraining samples with labels into an initial recognition model; updatingparameters of the initial recognition model through training; andobtaining the recognition model when the initial recognition modelsatisfies a preset condition; obtaining pedestrian distributioninformation in the preset area during a current time period based on thepopulation flow information; and receiving the pedestrian distributioninformation transmitted from the sensor network sub-platform;determining, based on the pedestrian distribution information, at leastone area location in the preset area for a future time period, apopulation flow load of the area location being greater than a firstthreshold, wherein the at least one area location is determined byprocessing the pedestrian distribution information through an arealocation prediction model, the area location prediction model includes agraph neural network model, and the area location prediction model istrained by the public place management platform according to a machinelearning algorithm based on at least one second training sample and atleast one second label, wherein each second training sample includessample pedestrian distribution information, and each second label is asample area location that is determined by an actual population flowload of the preset area in the sample pedestrian distributioninformation, and generating the sample area location including: theprocessing device of public place management platform designating anarea location that the actual population flow load of the preset area inthe sample pedestrian distribution information is greater than the firstthreshold as the sample area location; a graph input into the graphneural network model includes at least two nodes and at least one edge,each of the at least two nodes includes at least one public place site,and the at least one edge includes a relationship between the at leasttwo nodes; a node characteristic of each node includes a count ofentrances and exits of the at least one public place site, distributionpositions of the entrances and the exits, time information, nodeenvironment information, holiday information, or a size of a parkinglot; an edge characteristic of the at least one edge includes arelationship strength vector, wherein the relationship strength vectorreflects a relationship strength between two nodes, and the relationshipstrength vector is determined from a plurality of vectors, wherein theplurality of vectors include distance strength vectors, locationstrength vectors, and category strength vectors; the node characteristicincludes surrounding traffic flow; an output of the graph neural networkmodel is an area location that the population flow load is greater thanthe first threshold, and the area location is determined based on thecorrelation of each node in the graph composed of nodes and edges;determining, based on the area location, prompt information; andfeedbacking the prompt information to a user terminal of the userplatform through a service platform via network.
 9. A non-transitorycomputer readable medium on which a computer program is stored, whereinthe computer program, when executed by a processor, implements a methodfor managing a public place in a smart city, the method comprising:obtaining pedestrian distribution information in a preset area during acurrent time period via network from a storage device, including: aprocessor of a sensor network sub-platform processing a plurality ofvideos based on a recognition model; obtaining population flowinformation of a plurality of preset areas in the plurality of videos,wherein the recognition model includes a Yolo model that recognizesusers in the plurality of videos and the recognition model is obtainedthrough a training process, comprising: generating a plurality of firsttraining samples and first labels; wherein the first training samplesinclude sample videos obtained based on historical surveillance videos,the first labels include a sample object from the users and a categorycorresponding to an object box; inputting the first training sampleswith labels into an initial recognition model; updating parameters ofthe initial recognition model through training; and obtaining therecognition model when the initial recognition model satisfies a presetcondition; obtaining pedestrian distribution information in the presetarea during a current time period based on the population flowinformation; and receiving the pedestrian distribution informationtransmitted from the sensor network sub-platform; generating, based onthe pedestrian distribution information, at least one area location inthe preset area for a future time period, a population flow load of thearea location being greater than a first threshold, wherein the at leastone area location is determined by processing the pedestriandistribution information through an area location prediction model, thearea location prediction model includes a graph neural network model,and the area location prediction model is trained by the public placemanagement platform according to a machine learning algorithm based onat least one training sample and at least one label, wherein eachtraining sample includes sample pedestrian distribution information, andeach label is a sample area location that is determined by an actualpopulation flow load of the preset area in the sample pedestriandistribution information, wherein generating the sample area locationincludes: designating, by the processor of the public place managementplatform, a corresponding area location that the actual population flowload of the preset area in the sample pedestrian distributioninformation is greater than the first threshold as the sample arealocation; a graph input into the graph neural network model includes atleast two nodes and at least one edge, each of the at least two nodesincludes at least one public place site, and the at least one edgeincludes a relationship between the at least two nodes; a nodecharacteristic of each node includes a count of entrances and exits ofthe at least one public place site, distribution positions of theentrances and the exits, time information, node environment information,holiday information, or a size of a parking lot; an edge characteristicof the at least one edge includes a relationship strength vector,wherein the relationship strength vector reflects a relationshipstrength between two nodes, and the relationship strength vector isdetermined from a plurality of vectors, wherein the plurality of vectorsinclude distance strength vectors, location strength vectors, andcategory strength vectors; the node characteristic includes surroundingtraffic flow; an output of the graph neural network model is the arealocation, and the area location is determined based on a correlation ofeach node in the graph composed of nodes and edges; generating, based onthe area location, prompt information; and feedbacking the promptinformation to a user terminal of a user platform through a serviceplatform via network.